This work proposes a demo-free hierarchical memory VLA : a Qwen3-VL-4B planner with a persistent keyframe buffer, trained via streaming GRPO on dense task-completion reward from simulation, above a frozen GroundSG π 0 .
HAM-VLN is presented, a decision-coupled, agent-authored memory that equips the robot with a persistent, depth-grounded world graph and reduces the context length by more than 65% compared to previous methods.
An Liu, Bingxi Liu, Hongyu Ding et al.· arXiv.org· 0 citations
HyMeS, a hybrid learning framework that leverages the reasoning and memory-management capabilities of coding agents to steer a Markovian VLA for memory-dependent manipulation, is proposed, enabling data-efficient compositional generalization.
Yunhao Zhao, Zhenyang Ni, Haoyang Chen et al.· 0 citations
MemVLN is proposed, a novel VLN framework that achieves state-of-the-art performance with real-time inference efficiency (14 FPS) and introduces Procedural Memory for fast action with a compact vocabulary of atomic mid-level actions to bypass auto-regressive decoding latency.
Foresight Residual RL is proposed, which optimizes handoff quality by augmenting each subtask's sparse success reward with an offline-estimated foresight value -- the probability of future subtask success conditioned on the terminal state of the current subtask.
Yuhan Liu, Xinyu Zhang, Litao Liu et al.· arXiv.org· 1 citation
The proposed hierarchical long-horizon VLA architecture with an explicit language-memory module improves the success rate and robustness of VLA models on complex long-horizon tasks while providing an interpretable semantic account of the decision process.
Hness VLA is presented, a memory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive and composes it with a small fixed library of analytic primitives for grounding, staging, transport, navigation, and release.
